AI Enabled Software Engineering Eğitimi

Artificial intelligence is reshaping how software is designed, developed, tested, and maintained. This course explores how generative AI can be used as a practical development partner across the software engineering lifecycle.

Learners will gain hands-on experience using AI tools to support coding, debugging, documentation, and problem solving. The course places a strong emphasis on prompt engineering, helping participants produce structured, relevant, and reliable outputs from AI systems.

Through guided demonstrations, collaborative exercises, and real-world scenarios, learners will practise writing and refining prompts for common engineering tasks. They will also develop the critical thinking skills needed to evaluate AI-generated outputs and improve them through structured iteration.

By the end of the course, participants will be able to confidently integrate AI assistants into their daily workflows and use advanced prompting techniques to enhance productivity and software quality.

Participants should have:

• Basic software engineering experience, including programming concepts
• Familiarity with an integrated development environment such as Visual Studio Code
• Understanding of software development practices such as debugging, testing, and documentation
• An interest in applying AI tools to improve development efficiency

Target audience

This course is designed for:

• Software engineers and developers looking to integrate AI tools into their workflows
• Engineering teams aiming to improve productivity using generative AI
• Technical leads and architects exploring AI-assisted development capabilities
• DevOps engineers and technical professionals working closely with development teams

By the end of this course, learners will be able to:

• Identify high-impact use cases for generative AI in software engineering
• Explain the importance of human oversight when working with AI systems
• Configure and use an AI coding assistant within a development environment
• Write effective prompts for coding, debugging, and documentation tasks
• Critically evaluate and refine AI-generated outputs
• Integrate AI tools into day-to-day engineering workflows with confidence

Introduction to AI in software engineering

• Overview of generative AI and its role in modern development
• Key capabilities and limitations of AI coding assistants
• Understanding AI as a development partner
• Identifying suitable use cases across the software lifecycle

Setting up AI-assisted development environments

• Overview of AI coding tools and integrations
• Configuring AI assistants in development environments
• Working with extensions and plugins
• Security and privacy considerations when using AI tools

Prompt engineering fundamentals

• Principles of effective prompt design
• Structuring prompts for clarity and precision
• Techniques for improving output quality
• Common prompting mistakes and how to avoid them

Using AI for coding and development

• Generating code snippets and functions
• Refactoring and optimising existing code
• Exploring alternative implementations
• Supporting multiple programming languages and frameworks

Debugging and problem solving with AI

• Using AI to identify and fix errors
• Analysing logs and error messages
• Step-by-step debugging with AI assistance
• Validating and testing AI-suggested fixes

Documentation and knowledge generation

• Generating technical documentation
• Creating code comments and explanations
• Producing user guides and knowledge base content
• Maintaining consistency and accuracy in documentation

Evaluating and refining AI outputs

• Assessing relevance and correctness of AI responses
• Identifying hallucinations and inaccuracies
• Iterative prompt refinement techniques
• Establishing validation workflows

Collaborative workflows with AI

• Integrating AI into team development practices
• Using AI in code reviews and collaboration
• Balancing automation with human expertise
• Ethical considerations and responsible AI use

Applying AI in real-world scenarios

• End-to-end development scenarios
• Case studies of AI-assisted engineering
• Measuring productivity and efficiency gains
• Adapting workflows for continuous improvement

Exams and assessments

This course does not include a formal exam.

Hands-on learning

Learners will complete knowledge checks, instructor-led discussions, and practical lab exercises throughout the course to reinforce key concepts and validate their understanding.